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Scikit-learn

What is scikit-learn?

Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.cikit-learn provides a range of supervised and unsupervised learning algorithms via a consistent interface in Python. It is licensed under a permissive simplified BSD license and is distributed under many Linux distributions, encouraging academic and commercial use.

The library is built upon the SciPy (Scientific Python) that must be installed before you can use scikit-learn. This stack that includes:

  • NumPy: Base n-dimensional array package
  • SciPy: Fundamental library for scientific computing
  • Matplotlib: Comprehensive 2D/3D plotting
  • IPython: Enhanced interactive console
  • Sympy: Symbolic mathematics
  • Pandas: Data structures and analysis

Extensions or modules for SciPy care conventionally named SciKits. As such, the module provides learning algorithms and is named scikit-learn.The vision for the library is a level of robustness and support required for use in production systems. This means a deep focus on concerns such as easy of use, code quality, collaboration, documentation and performance.Although the interface is Python, c-libraries are leverage for performance such as numpy for arrays and matrix operations, LAPACK, LibSVM and the careful use of cython.

What is Scikit-learn used for?

Scikit-learn (Sklearn) is the most useful and robust library for machine learning in Python. It provides a selection of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction via a consistence interface in Python. This library, which is largely written in Python, is built upon NumPy, SciPy and Matplotlib.Scikit-learn is an indispensable part of the Python machine learning toolkit at JPMorgan. It is very widely used across all parts of the bank for classification, predictive analytics, and very many other machine learning tasksSciPy in Python is an open-source library used for solving mathematical, scientific, engineering, and technical problems. It allows users to manipulate the data and visualize the data using a wide range of high-level Python commands. SciPy is built on the Python NumPy extention.

Feature of scikit-learn -:

Scikit-learn library is focused on modeling the data. Some of the most popular groups of models provided by Sklearn are as follows −

  • Supervised Learning algorithms − Almost all the popular supervised learning algorithms, like Linear Regression, Support Vector Machine (SVM), Decision Tree etc., are the part of scikit-learn.

  • Unsupervised Learning algorithms − On the other hand, it also has all the popular unsupervised learning algorithms from clustering, factor analysis, PCA (Principal Component Analysis) to unsupervised neural networks.

  • Clustering − This model is used for grouping unlabeled data.

  • Cross Validation − It is used to check the accuracy of supervised models on unseen data.

  • Dimensionality Reduction − It is used for reducing the number of attributes in data which can be further used for summarisation, visualisation and feature selection.

  • Ensemble methods − As name suggest, it is used for combining the predictions of multiple supervised models.

  • Feature extraction − It is used to extract the features from data to define the attributes in image and text data.

  • Feature selection − It is used to identify useful attributes to create supervised models.

  • Open Source − It is open source library and also commercially usable under BSD license

Scikit-Learn vs. TensorFlow:

Scikit-Learn and TensorFlow are both designed to help developers create and benchmark new models, so their functional implementations are quite similar with the key distinction that Scikit-Learn is used in practice with a wider scope of models as opposed to TensorFlow’s implied use for neural networks.

Scikit-Learn implements all of its machine learning algorithms as a base estimator and TensorFlow mirrors this terminology in its estimator class. Both frameworks’ estimators have abstract methods that are used by the framework to train and evaluate the estimator to ease head-to-head comparisons.

TensorFlow estimators and Scikit-Learn estimators are alike, but Scikit-Learn estimators are generally more flexible with other frameworks such as XGBoost, while TensorFlow estimators are intended to be built using TensorFlow core functionality which is optimized for neural networks.

Scikit-Learn does implement some barebones neural network models, but off-the-shelf doesn’t support more complex neural networks and the higher level of the customizability of TensorFlow.

In effect, Scikit-Learn often abstracts many of the details of the machine learning model away from the developer while the developer must implement details and inner-workings of their TensorFlow models. With this distinction comes a trade-off of speed, as the more flexible framework cannot achieve the performance of the specialized framework.

Scikit-Learn’s generality makes it useful for comparing entirely different types of machine learning models against each other; TensorFlow’s specialization enables under-the-hood optimizations, making it easier and more efficient to compare different TensorFlow and neural network models. For this reason, Scikit-Learn is often used to initially select the models you’ll later improve in greater detail.

TensorFlow’s availability in more languages and a greater focus on optimizations also makes it the preferred choice for deploying neural network models to production, as you can develop specifically for your target platform and squeeze out the greatest efficiency.

How can you use Scikit-Learn and TensorFlow together?

Since Scikit-Learn allows you to implement your own estimators, there’s nothing stopping you from using TensorFlow within Scikit-Learn’s framework to compare TensorFlow models against other Scikit-Learn models. This flexibility is extremely useful, as it allows you to determine whether to delve into the depths of TensorFlow models or to pursue a different Scikit-Learn model.

Both Scikit-Learn and TensorFlow are useful enough that they are likely to find a place in your development pipeline—but you must be mindful to use them to their advantages.